Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Quantitative Approaches to enable the Automated Planning of Adaptive Process Models

Nowadays, process models are valuable tools for a variety of activities in the business environment. They are used, for example, to train employees, to document processes or as part of company audits and to align the IT strategy with the company goals. However, process models are still created manually in many cases. This manual creation proves to be tedious, thus cost-intensive and especially error-prone. The dissertation at hand addresses this problem area and presents approaches for the automated planning of adaptive process models. Adaptive process models are those process models that take into account factors that require flexibility in processes. This includes, for example, the context of processes or the actors involved in the process